Updated flight data

Motivation

I found a problem with the previous code: I added info on butterfly_flight to the data before calculating the distances and that makes the paths larger than they actually are. This version does not have that problem.

I also changed the code in order to use purrr, a package for doing functional programming. Basically, it makes it way easier to maintain the code by making it shorter.

I suppose that’s enough rambling from me, let’s give you the tables, that’s what you want after all 😄

The code for generating this document lives in this repo, in the file 06-updated-flight.Rmd.

Data processing

Loads and installs packages used if needed

list_of_packages <- c("tidyverse", "here", "fs", "plotly", "DT", "signal")
new_packages <-
  list_of_packages[!(list_of_packages %in% installed.packages()[, "Package"])]
if (length(new_packages))
  install.packages(new_packages)

library(tidyverse)
library(here)
library(fs)
library(plotly)
library(DT)
library(signal)

Loading files

fs::dir_ls(here::here('wand'), recurse = T, regexp = "unpaired-points-xyz.csv$") %>%
  purrr::map_dfr(readr::read_csv, .id = "source") -> all_flights

Removing NaNs

all_flights %>% 
  tidyr::drop_na() -> all_flights

Defining functions

calc_flight_dist <- function(dat) {
  dat_dist <-  {{dat}} %>% 
  dist() %>%  # calculates distance
  as.matrix()  # converts it to a matrix

return(sum((dat_dist[row(dat_dist) == col(dat_dist) + 1])))  # sums the distance between points by summing the points in line n and column n + 1
  
}

plots_flight_path_raw <- function(dat) {
  
plotly::plot_ly(data = dat,
        type = "scatter3d", 
        x = dat$x_1,
        y = dat$y_1,
        z = dat$z_1,
        mode = "lines")
}

plots_flight_path_butterworth <- function(dat) {
  
plotly::plot_ly(data = dat,
        type = "scatter3d", 
        x = dat$x_butterworth,
        y = dat$y_butterworth,
        z = dat$z_butterworth,
        mode = "lines")
}

calc_straight_dist <- function(dat) {
  
dist_dat <- 
  dat %>% 
  dist() %>% 
  as.matrix()

return(dist_dat[1, nrow(dist_dat)])  
}

# creating butterworth filter

butterworth_filter <-
  signal::butter(n = 4,  # order
                 W = 0.5,  # critical frequency
                 type = "low")

all_flights %>%
  dplyr::group_by(source) %>%
  dplyr::mutate(
    x_butterworth = signal::filter(butterworth_filter, x_1),
    y_butterworth = signal::filter(butterworth_filter, y_1),
    z_butterworth = signal::filter(butterworth_filter, z_1)
  ) %>%
  tidyr::nest(data_raw = ends_with("_1"),
              data_butterworth = ends_with("_butterworth")) %>%
  dplyr::mutate(
    butterfly = stringr::str_extract(source, pattern = "b\\d{3}"),
    take = stringr::str_extract(source, pattern = "t\\d{1}"),
    distance_raw = purrr::map_dbl(data_raw, ~ calc_flight_dist(.x)),
    straight_distance_raw = purrr::map_dbl(data_raw, ~ calc_straight_dist(.x)),
    sinuosity_raw = distance_raw / straight_distance_raw,
    plot_3d_raw = purrr::map(data_raw, ~ plots_flight_path_raw(.x)),
    distance_butterworth = purrr::map_dbl(data_butterworth, ~ calc_flight_dist(.x)),
    straight_distance_butterworth = purrr::map_dbl(data_butterworth, ~ calc_straight_dist(.x)),
    sinuosity_butterworth = distance_butterworth / straight_distance_butterworth,
    plot_3d_butterworth = purrr::map(data_butterworth, ~ plots_flight_path_butterworth(.x))
  ) %>%
  dplyr::ungroup() -> processed

Here’s the table

processed %>%  
  dplyr::select(butterfly, take, everything(), -source, -data_raw, -data_butterworth, -plot_3d_raw, -plot_3d_butterworth) %>% 
  DT::datatable(extensions = 'Buttons',
                options = list(dom = 'Blfrtip',
                               buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
                               lengthMenu = list(c(10,25,50,-1),
                                                 c(10,25,50,"All"))))